現狀數據為一種區間設限資料，其觀察值僅包括檢查時間和事件發生時間是否在檢查時間之前發生。探討事件發生時間和共變量之間的關係之半母數迴歸方法，在現狀數據中已被廣泛地研究。在本論文中，我們考慮在比例勝算比模型下之現狀數據，對於共變量效應之概似比檢定。並提出一個簡單的演算法則來計算此檢定量。此演算法是根據一系列的自身一致方程式且我們利用收縮原理來證明其收斂性。我們進行了模擬計算並分析兩筆真實數據，來說明此概似比檢定量之卡方漸進性的適當性和此演算法的可行性。

Current status data result from a simple form of interval censoring in which the observation consists only of an examination time and knowledge of whether the failure time has occurred before the exam. Semiparametric regression methods which examine the relationship between the failure time and covariates have been studied extensively for current status data. In this thesis, we consider the likelihood ratio test for testing covariate effect under the proportional odds model with current status data and propose an easily implemented algorithm for computing the statistics. The algorithm proposed is based on a set of self-consistency equations and its convergence is proved by contraction principle. The adequacy of the Chi-squared approximation for likelihood ratio statistics and the availability of the algorithm are demonstrated in simulation studies and in the analyses of two real data.

致謝.......................................................I

論文中文摘要................................................II

論文英文摘要...............................................III

第一節 前言.................................................1

第二節 資料與模型敘述.........................................2

  2.1 資料與模型............................................2

  2.2 概似比檢定............................................3

第三節 演算法................................................4

第四節 模擬試驗..............................................8

第五節 實例分析.............................................17

5.1 肺腫瘤數據分析..........................................17

5.2 水晶體鈣化數據分析.......................................18

第六節 結論................................................19

參考文獻...................................................20

附 錄  A..................................................22
附 錄  B..................................................23

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